BIO-INSPIRED IMAGING
nature Inspired by
Matthew Dale investigates a new class of highly-efficient image sensor that’s just starting to find its way onto the commercial market, all based on the principles of biological sight
T
he biological eye is a marvel of nature refined over millions of years of evolution. Machine vision manufacturers and researchers have
taken inspiration from nature to produce new vision systems that emulate the way eyes see, completely overthrowing the traditional machine vision architectures used over the past half a century. Conventional image sensors capture information
by receiving signals from a timer that exposes their array of pixels for a fixed period, regardless of what happens in the field of view. Tis leads to data being captured unnecessarily if nothing happens in the scene, or for data to be lost if an event happens very quickly between frames. In contrast, biological vision systems are efficient,
allowing the brain to process huge amounts of visual input without using too much energy. By taking inspiration from this frameless paradigm and translating it to dynamic vision sensors, image acquisition can be transferred from an array of pixels to a set of smart pixels that can control how data is captured individually. Tese smart pixels are able to deliver visual
information more efficiently, by responding only to occurrences within their field of view – such as a change in lighting or movement – similar to how the individual cells of an eye respond to temporal changes. Tey are then able to self-adjust their acquisition speeds and exposure times depending on the change, meaning that if an event happens at speed, they acquire data quickly, while if less is happening, they can slow down their capture appropriately. Exposure can also be adjusted according to brightness, which removes problems associated with over and under exposure.
‘Te pixels monitor increases and decreases in
photocurrent that are beyond a threshold relative to the initial amount of illumination,’ explained Dr Simeon Bamford, CTO of Swiss neuromorphic vision firm Inivation. ‘Te response of the sensor is [therefore] approximately equivalent, whether in very high illumination or very low illumination.’ Tis reflects the way eyes work, which react to lighting changes in a scene. By responding to only the changes of a scene,
bio-inspired sensors output a sparse stream of events rather than a sequence of images, each encoding the location of the pixel that sent it and whether the change was positive or negative. Tis results in a large reduction in data acquisition and processing, according to Luca Verre, co-founder and CEO of bio-inspired vision firm Chronocam, as well as reductions in information loss, as data can no longer be lost between frames if an event occurs
14 Imaging and Machine Vision Europe • December 2017/January 2017
too quickly. ‘We can control the bandwidth and the sensitivity of the sensor by regions of interest, and can exclude a region of interest from sending out information … so we can actually further compress the data,’ Verre added. Te signals from events also occur on a scale
of microseconds, enabling very low latency vision as each frame doesn’t have to be exposed and transmitted. Overall, this translates to imaging systems that use much less power, as little computation has to be done when picking out the relevant elements of a scene. On top of these benefits, according to Verre, by
having each pixel adjust independently, a very high dynamic range can be achieved. Te difference between this and our own vision’s high dynamic range is that a dynamic vision sensor can adapt to light changes in microseconds, whereas the eye can take some minutes to adapt.
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